Spatiotemporal Patterns and Determinants of Cropland Abandonment in Mountainous Regions of China: A Case Study of Sichuan Province

• Published in: Land (Basel) Vol. 14; no. 3; p. 647
• Main Authors: Hong, Buting, Wang, Jicheng, Xiao, Jiangtao, Yuan, Quanzhi, Ren, Ping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Abandonment; Agricultural land; Agricultural production; Biodiversity; Carbon sequestration; Case studies; Climate change; Clustering; cropland abandonment; Crops; Digital Elevation Models; Farmers; Food production; Food security; GDP; Geospatial data; Gross Domestic Product; hierarchical linear model; Households; Hunger; influencing factors; Land area; Mountain regions; Mountainous areas; Mountains; Population; Remote sensing; Rural areas; Sichuan Province; spatiotemporal patterns; Sustainable agriculture; Sustainable development; Urban areas; Urbanization; China; Sichuan China
• DOI: 10.3390/land14030647
• ISSN: 2073-445X

Cropland abandonment (CA) is an increasingly severe global issue, with significant implications for achieving the Sustainable Development Goal of Zero Hunger. In China, widespread CA is particularly evident in remote mountainous regions. However, the rugged terrain and highly fragmented cropland pose significant challenges in mapping abandoned cropland with high precision using remote sensing technology. Moreover, CA is the result of multi-level factors, yet previous studies have primarily analyzed its driving factors from a single level, leading to a lack of comprehensive understanding of the underlying mechanisms. We took Sichuan Province, located in the mountainous regions of Western China, as a case study, utilizing satellite-derived high-precision CA maps to reveal the spatiotemporal patterns of CA. Additionally, we employed hierarchical linear model to explore the determinants of CA and their interactions at both county and municipal levels. The results indicate that the CA rate decreased continuously from 6.75% in 2019 to 4.47% in 2023, with abandoned cropland exhibiting significant spatial clustering. High-value clusters were predominantly concentrated in the western mountainous areas, and hotspots of CA exhibited a general migration trend from the northeast to the southwest. Furthermore, we found that CA is influenced by multi-level factors, with 61% and 39% of the variance in CA being explained at the county and municipal levels, respectively. The agglomeration index of cropland (AI) is a key determinant at the county level, with the Digital Elevation Model (DEM) and the distance to roads also playing significant roles. At the municipal level, urbanization rate and the proportion of non-agricultural employment (PNAE) are dominant factors, and an increase in PNAE weakens the negative impact of AI on CA rates. To curb CA in mountainous areas, we recommend implementing land consolidation projects, improving rural land transfer markets, and strengthening legal mechanisms to combat CA. Our study has broad application prospects, providing critical support for assessing the ecological and environmental consequences of CA and exploring the potential of reutilizing abandoned cropland for food production, bioenergy, and carbon sequestration.